This event has been UPDATED since it was first published. View the Complete, UPDATED & Detailed Data Science with Python Training Information here.

5 Weeks Only Data Science with Python Training course is being delivered from November 16, 2020 - December 16, 2020 for 20 Hours over 5 weeks, 10 sessions, 2 sessions per week, 2 hours per session.

• All Published Ticket Prices are in US Dollars
• The course will be taught in English language

5 Weeks Only Data Science with Python Training Course Schedule

• November 16, 2020 - December 16, 2020 US Pacific time
• 5 Weeks | 2 Hours on Mondays, 2 Hours on Wednesdays every week US Pacific time
• 5:30 PM - 7:30 PM US Pacific time each of those days
• Please click here to add your city name and check your local date and time for the first session to be held on November 16, 2020 at 5:30 PM US Pacific Time.

Features and Benefits

• 20 Hours, 10 sessions, 5 weeks of total Instructor-led and guided training
• Training material, instructor handouts and access to useful resources on the cloud provided
• Practical Hands-on Lab exercises provided
• Actual code and scripts provided
• Real-life Scenarios

Course Prerequisites

Some background in python programming may be helpful.

Who can take this course?

• Software engineers and data analysts
• Business intelligence professionals
• Those aspiring for a career in data science
• Professionals and Students looking to enter the Data Science industry

Course Outline*

*The actual course content covered during the training may slightly vary based on the background and expectations of the students and instructor preference.

1. Descriptive Statistics

• Mean/Median
• Variance/ Std Deviation
• Quartile/Box Plot/IQR/Percentile
• Outlier
• Z Score
• Co-variance / Co-relation

2. Inferential Statistics and Exploratory Data Analysis

• Probability and Random Variables
• Population and Sample
• Normal Distribution, Binomial Distribution
• Testing of Hypothesis
• t Test / ANOVA/ Chi Square Test

3. Machine Learning Models

• Linear Regression
• Logistic Regression
• Naïve Bayes
• K Nearest Neighbors
• Decision Tree
• Support Vector Machine
• Ensemble Techniques

4. Advanced Machine Learning Techniques

• Model Evaluation
• Ridge Regression
• Lasso Regression
• Grid Search Cross validation

5. Unsupervised Learning

• Clustering
• PCA
• Apriori Algorithm

6. Time Series Forecasting

• Moving Average
• Exponential Moving Average
• ARIMA